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    A New Evidence Model for Missing Data Speech Recognition With Applications in Reverberant Multi-Source Environments

    Access Status
    Fulltext not available
    Authors
    Kuhne, M.
    Togneri, R.
    Nordholm, Sven
    Date
    2011
    Type
    Journal Article
    
    Metadata
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    Citation
    Kuhne, Marco and Togneri, Roberto and Nordholm, Sven. 2011. A New Evidence Model for Missing Data Speech Recognition With Applications in Reverberant Multi-Source Environments. IEEE Transactions on Audio, Speech, and Language Processing. 19 (2): pp. 372-384.
    Source Title
    IEEE Transactions on Speech and Audio Processing
    DOI
    10.1109/TASL.2010.2048604
    ISSN
    1063-6676
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/36504
    Collection
    • Curtin Research Publications
    Abstract

    Conventional hidden Markov model (HMM) decoders often experience severe performance degradations in practice due to their inability to cope with uncertain data in time-varying environments. In order to address this issue, we propose the bounded-Gauss-Uniform mixture probablity density function (pdf) as a new class of evidence model for missing data speech recognition. Exemplary for a hands-free speech recognition scenario, we illustrate how the parameters of the new mixture pdf can be estimated with the help of a multi-channel source separation front-ed. In comparison with other models the new evidence pdf retains a fuller description of the available data and provides a more effective link between source separation and recognition. The superiority of the bounded-Gauss-Uniform mixture pdf over conventional approaches is demonstrated for a connected digits recognition task under varying test conditions.

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